Tabula muris
Collection
Data underlying Tabula muris senis
•
43 items
•
Updated
Popular Vote (popV) model for automated cell type annotation of single-cell RNA-seq data. We provide here pretrained models for plug-in use in your own analysis. Follow our tutorial to learn how to use the model for cell type annotation.
Model description
Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death. Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood. To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing—such as senescence, genomic instability and changes in the immune system. This transcriptomic atlas—which we denote Tabula Muris Senis, or ‘Mouse Ageing Cell Atlas’—provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types.
Link to CELLxGENE: Link to the data in the CELLxGENE browser for interactive exploration of the data and download of the source data.
Training Code URL: Not provided by uploader.
Metrics
We provide here accuracies for each of the experts and the ensemble model. The validation set accuracies are computed on a 10% random subset of the data that was not used for training.
| Cell Type | N cells | celltypist | knn bbknn | knn harmony | knn on scvi | onclass | scanvi | svm | xgboost | Consensus Prediction |
|---|---|---|---|---|---|---|---|---|---|---|
| endothelial cell | 223 | 0.99 | 0.99 | 0.99 | 0.98 | 0.00 | 0.98 | 1.00 | 1.00 | 1.00 |
| oligodendrocyte | 197 | 0.99 | 0.99 | 0.99 | 0.99 | 0.00 | 0.99 | 1.00 | 0.99 | 0.99 |
| astrocyte | 60 | 0.96 | 0.98 | 0.97 | 0.92 | 0.00 | 0.94 | 0.98 | 0.97 | 0.98 |
| brain pericyte | 46 | 0.98 | 0.98 | 1.00 | 0.93 | 0.00 | 0.97 | 1.00 | 1.00 | 1.00 |
| neuron | 40 | 0.93 | 0.94 | 0.89 | 0.64 | 0.00 | 0.76 | 0.95 | 0.91 | 0.93 |
| oligodendrocyte precursor cell | 34 | 0.97 | 0.97 | 0.97 | 0.97 | 0.00 | 0.99 | 0.99 | 0.96 | 0.97 |
| interneuron | 26 | 0.96 | 0.95 | 0.91 | 0.56 | 0.00 | 0.70 | 0.96 | 0.90 | 0.96 |
| neuroepithelial stem cell | 15 | 0.87 | 0.93 | 0.90 | 0.65 | 0.00 | 0.74 | 1.00 | 0.90 | 0.97 |
| microglial cell | 12 | 1.00 | 0.96 | 0.92 | 0.48 | 0.00 | 0.92 | 1.00 | 0.96 | 1.00 |
| CD8-positive, alpha-beta T cell | 16 | 0.91 | 0.91 | 0.91 | 0.91 | 0.00 | 0.74 | 0.88 | 0.97 | 0.94 |
| medium spiny neuron | 10 | 0.95 | 0.91 | 0.91 | 0.52 | 0.00 | 0.80 | 0.95 | 0.91 | 0.91 |
| neural stem cell | 10 | 0.75 | 0.67 | 0.71 | 0.90 | 0.00 | 0.95 | 0.84 | 0.74 | 0.78 |
| T cell | 3 | 0.57 | 0.67 | 0.67 | 0.00 | 0.00 | 0.25 | 0.57 | 0.80 | 0.67 |
| ependymal cell | 6 | 0.86 | 1.00 | 1.00 | 0.67 | 0.00 | 0.80 | 1.00 | 0.83 | 1.00 |
| Bergmann glial cell | 5 | 0.75 | 0.89 | 0.89 | 0.00 | 0.00 | 0.57 | 0.89 | 0.89 | 0.89 |
| macrophage | 1 | 1.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.50 | 1.00 | 1.00 | 1.00 |
| mature NK T cell | 2 | 0.00 | 0.00 | 0.50 | 0.00 | 0.00 | 0.57 | 0.50 | 1.00 | 0.67 |
The train accuracies are computed on the training data.
| Cell Type | N cells | celltypist | knn bbknn | knn harmony | knn on scvi | onclass | scanvi | svm | xgboost | Consensus Prediction |
|---|---|---|---|---|---|---|---|---|---|---|
| endothelial cell | 2009 | 0.99 | 0.99 | 0.99 | 0.99 | 0.00 | 0.98 | 1.00 | 1.00 | 1.00 |
| oligodendrocyte | 1896 | 0.99 | 1.00 | 1.00 | 1.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| astrocyte | 527 | 0.96 | 0.97 | 0.98 | 0.96 | 0.00 | 0.96 | 1.00 | 1.00 | 0.99 |
| brain pericyte | 438 | 0.98 | 0.98 | 1.00 | 0.97 | 0.00 | 0.97 | 1.00 | 1.00 | 1.00 |
| neuron | 435 | 0.95 | 0.91 | 0.90 | 0.82 | 0.00 | 0.74 | 1.00 | 0.99 | 0.98 |
| oligodendrocyte precursor cell | 278 | 0.98 | 0.97 | 0.99 | 0.97 | 0.00 | 0.99 | 1.00 | 1.00 | 1.00 |
| interneuron | 213 | 0.95 | 0.90 | 0.90 | 0.70 | 0.00 | 0.74 | 1.00 | 1.00 | 0.98 |
| neuroepithelial stem cell | 136 | 0.83 | 0.85 | 0.88 | 0.76 | 0.00 | 0.77 | 1.00 | 0.99 | 0.98 |
| microglial cell | 126 | 0.97 | 0.95 | 0.93 | 0.77 | 0.00 | 0.95 | 0.99 | 0.99 | 0.99 |
| CD8-positive, alpha-beta T cell | 88 | 0.86 | 0.86 | 0.92 | 0.77 | 0.00 | 0.95 | 1.00 | 1.00 | 0.97 |
| medium spiny neuron | 93 | 0.99 | 0.98 | 0.93 | 0.81 | 0.00 | 0.95 | 1.00 | 1.00 | 0.99 |
| neural stem cell | 86 | 0.88 | 0.87 | 0.96 | 0.93 | 0.00 | 0.97 | 0.99 | 0.99 | 0.99 |
| T cell | 62 | 0.72 | 0.66 | 0.75 | 0.53 | 0.00 | 0.85 | 1.00 | 1.00 | 0.97 |
| ependymal cell | 49 | 0.91 | 0.97 | 0.99 | 0.85 | 0.00 | 0.85 | 1.00 | 0.99 | 0.98 |
| Bergmann glial cell | 49 | 0.90 | 0.95 | 0.94 | 0.62 | 0.00 | 0.85 | 1.00 | 1.00 | 0.98 |
| macrophage | 24 | 0.60 | 0.00 | 0.73 | 0.56 | 0.00 | 0.88 | 1.00 | 1.00 | 0.98 |
| mature NK T cell | 15 | 0.00 | 0.00 | 0.77 | 0.00 | 0.00 | 0.88 | 1.00 | 1.00 | 0.80 |
References
A single-cell transcriptomic atlas characterizes ageing tissues in the mouse, The Tabula Muris Consortium, Nicole Almanzar, Jane Antony, Ankit S. Baghel, Isaac Bakerman, Ishita Bansal, Ben A. Barres, Philip A. Beachy, Daniela Berdnik, Biter Bilen, Douglas Brownfield, Corey Cain, Charles K. F. Chan, Michelle B. Chen, Michael F. Clarke, Stephanie D. Conley, Spyros Darmanis, Aaron Demers, Kubilay Demir, Antoine de Morree, Tessa Divita, Haley du Bois, Hamid Ebadi, F. Hernán Espinoza, Matt Fish, Qiang Gan, Benson M. George, Astrid Gillich, Rafael Gòmez-Sjöberg, Foad Green, Geraldine Genetiano, Xueying Gu, Gunsagar S. Gulati, Oliver Hahn, Michael Seamus Haney, Yan Hang, Lincoln Harris, Mu He, Shayan Hosseinzadeh, Albin Huang, Kerwyn Casey Huang, Tal Iram, Taichi Isobe, Feather Ives, Robert C. Jones, Kevin S. Kao, Jim Karkanias, Guruswamy Karnam, Andreas Keller, Aaron M. Kershner, Nathalie Khoury, Seung K. Kim, Bernhard M. Kiss, William Kong, Mark A. Krasnow, Maya E. Kumar, Christin S. Kuo, Jonathan Lam, Davis P. Lee, Song E. Lee, Benoit Lehallier, Olivia Leventhal, Guang Li, Qingyun Li, Ling Liu, Annie Lo, Wan-Jin Lu, Maria F. Lugo-Fagundo, Anoop Manjunath, Andrew P. May, Ashley Maynard, Aaron McGeever, Marina McKay, M. Windy McNerney, Bryan Merrill, Ross J. Metzger, Marco Mignardi, Dullei Min, Ahmad N. Nabhan, Norma F. Neff, Katharine M. Ng, Patricia K. Nguyen, Joseph Noh, Roel Nusse, Róbert Pálovics, Rasika Patkar, Weng Chuan Peng, Lolita Penland, Angela Oliveira Pisco, Katherine Pollard, Robert Puccinelli, Zhen Qi, Stephen R. Quake, Thomas A. Rando, Eric J. Rulifson, Nicholas Schaum, Joe M. Segal, Shaheen S. Sikandar, Rahul Sinha, Rene V. Sit, Justin Sonnenburg, Daniel Staehli, Krzysztof Szade, Michelle Tan, Weilun Tan, Cristina Tato, Krissie Tellez, Laughing Bear Torrez Dulgeroff, Kyle J. Travaglini, Carolina Tropini, Margaret Tsui, Lucas Waldburger, Bruce M. Wang, Linda J. van Weele, Kenneth Weinberg, Irving L. Weissman, Michael N. Wosczyna, Sean M. Wu, Tony Wyss-Coray, Jinyi Xiang, Soso Xue, Kevin A. Yamauchi, Andrew C. Yang, Lakshmi P. Yerra, Justin Youngyunpipatkul, Brian Yu, Fabio Zanini, Macy E. Zardeneta, Alexander Zee, Chunyu Zhao, Fan Zhang, Hui Zhang, Martin Jinye Zhang, Lu Zhou, James Zou; Nature, doi: https://doi.org/10.1038/s41586-020-2496-1
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API:
The model has no pipeline_tag.